A python software that can help blind people find things like laptops, phones, etc the same way a guide dog guides a blind person in finding his way.

Related tags

Deep LearningGuid-Eye
Overview

GuidEye

A python software that can help blind people find things like laptops, phones, etc the same way a guide dog guides a blind person in finding his way.

The problem GuidEye solves

Consider a scenario. A blind person is alone at home and needs something like he wants to find his movable chair but has forgotten where it was placed. Normally he would have to guess the direction and try by reaching out to the distances if there is no one to help. In this case he would have to waste a lot of energy and time and there is also a risk of them hurting or wounding themselves in the process. Our project solves this problem.

A person using this would simply open this project, it would ask him what he wants and he would simply say "chair", the project would detect and start searching for the chair with the help of machine learning item detection using the camera of the device. The person then would have to simply rotate and would try to find the chair using his phone. As soon as the software detects a chair, it calls out just like a metal detector beeps. Following this sound, the person can find the object he wants (in this case a chair) and reach to it with ease.

Thus, this software acts like a guide dog helping out a blind person

Challenges we ran into

Challenges we ran into while coming up and implementing this project :

  1. We wanted to solve a problem for blind people but were confused how could we do it.
  2. After coming up with this idea voice recognition and speaker acted as a small problem but was resolved.
  3. Training a model for detecting posed a problem.
  4. After this, coming up with a way of transferring and connecting a phone to the software

Technologies used

1.TensorFlow 2.OpenCV 3.PythonSpeech 4.RecognitionText-to-Speech 5.SSD

Owner
Munal Jain
Munal is currently a 3rd Year student in the IT branch at Shri GS Institute of Technology & Science, Indore.
Munal Jain
A Comprehensive Study on Learning-Based PE Malware Family Classification Methods

A Comprehensive Study on Learning-Based PE Malware Family Classification Methods Datasets Because of copyright issues, both the MalwareBazaar dataset

8 Oct 21, 2022
Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

Machine Learning University: Accelerated Tabular Data Class This repository contains slides, notebooks, and datasets for the Machine Learning Universi

AWS Samples 916 Dec 23, 2022
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 09, 2022
PyTorch implementation of MoCo: Momentum Contrast for Unsupervised Visual Representation Learning

MoCo: Momentum Contrast for Unsupervised Visual Representation Learning This is a PyTorch implementation of the MoCo paper: @Article{he2019moco, aut

Meta Research 3.7k Jan 02, 2023
codebase for "A Theory of the Inductive Bias and Generalization of Kernel Regression and Wide Neural Networks"

Eigenlearning This repo contains code for replicating the experiments of the paper A Theory of the Inductive Bias and Generalization of Kernel Regress

Jamie Simon 45 Dec 02, 2022
Multiple-criteria decision-making (MCDM) with Electre, Promethee, Weighted Sum and Pareto

EasyMCDM - Quick Installation methods Install with PyPI Once you have created your Python environment (Python 3.6+) you can simply type: pip3 install

Labrak Yanis 6 Nov 22, 2022
StyleGAN - Official TensorFlow Implementation

StyleGAN — Official TensorFlow Implementation Picture: These people are not real – they were produced by our generator that allows control over differ

NVIDIA Research Projects 13.1k Jan 09, 2023
Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI, 2020)

🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)

Qingyong 1.4k Jan 08, 2023
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer This is the official pytorch implementation of the CoTr: Paper: CoTr: Ef

218 Dec 25, 2022
NovelD: A Simple yet Effective Exploration Criterion

NovelD: A Simple yet Effective Exploration Criterion Intro This is an implementation of the method proposed in NovelD: A Simple yet Effective Explorat

29 Dec 05, 2022
Learning Confidence for Out-of-Distribution Detection in Neural Networks

Learning Confidence Estimates for Neural Networks This repository contains the code for the paper Learning Confidence for Out-of-Distribution Detectio

235 Jan 05, 2023
An implementation of the 1. Parallel, 2. Streaming, 3. Randomized SVD using MPI4Py

PYPARSVD This implementation allows for a singular value decomposition which is: Distributed using MPI4Py Streaming - data can be shown in batches to

Romit Maulik 44 Dec 31, 2022
[CVPR 2021] Forecasting the panoptic segmentation of future video frames

Panoptic Segmentation Forecasting Colin Graber, Grace Tsai, Michael Firman, Gabriel Brostow, Alexander Schwing - CVPR 2021 [Link to paper] We propose

Niantic Labs 44 Nov 29, 2022
A little Python application to auto tag your photos with the power of machine learning.

Tag Machine A little Python application to auto tag your photos with the power of machine learning. Report a bug or request a feature Table of Content

Florian Torres 14 Dec 21, 2022
CATE: Computation-aware Neural Architecture Encoding with Transformers

CATE: Computation-aware Neural Architecture Encoding with Transformers Code for paper: CATE: Computation-aware Neural Architecture Encoding with Trans

16 Dec 27, 2022
Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction".

GNN_PPI Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction". Lear

Ursa Zrimsek 2 Dec 14, 2022
Complete the code of prefix-tuning in low data setting

Prefix Tuning Note: 作者在论文中提到使用真实的word去初始化prefix的操作(Initializing the prefix with activations of real words,significantly improves generation)。我在使用作者提供的

Andrew Zeng 4 Jul 11, 2022
AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Ne

MALL Lab (IISc) 56 Dec 03, 2022
Fair Recommendation in Two-Sided Platforms

Fair Recommendation in Two-Sided Platforms

gourabgggg 1 Nov 10, 2021
Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation".

PixelTransformer Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation". Project Page Installation Please insta

Shubham Tulsiani 24 Dec 17, 2022